As data services and multimedia services grow rapidly, demands for locating and navigation, particularly in complicated indoor environments, increase as well. Statistical results show that on average, people spend 80% to 90% of their daily hours indoors, mobile phones operate indoors for 70% of their service time, and data connections are active indoors for 80% of their service time. It is always necessary to determine positional information of a mobile terminal or its holder in an environment such as an airport, an exhibition hall, a warehouse, a supermarket, a library, an underground parking lot or a mine. Further, indoor locating technologies provide a promising market prospect of commercial locating services including emergency call service, business help phone service, personal inquiry service, vehicle navigation service, specific tracking service and etc. The indoor locating technologies is being studied by researchers all over the world, such as cellular networks, wireless local area networks, Bluetooth, ultra-wideband, or image analyzing, are proposed. Radio Frequency Pattern Matching (RFPM) is a commonly used indoor locating solution.
RFPM considers electrical signal characteristics of a User Equipment (UE) at a certain position as unchanged, therefore, some electrical signal information related to a position can be used as “positional fingerprint” information of the position. A UE is located by measuring electrical signal information of the UE and comparing the electrical signal information of the UE with a “positional fingerprint” in a database to determine the current position of the UE. In RFPM, the position of the UE is recognized by using the database storing electrical signal characteristics of different positions. The locating process of the RFPM generally includes two stages: an offline sampling stage and an online locating stage.
In the offline sampling stage, firstly a coverage area of a radio network is divided into a plurality of grids. Then a fingerprint database is created for each grid, that is, electrical signal characteristics of surrounding base stations, e.g., RSRP of a Long Term Evolution (LTE) system, are stored in the fingerprint database. Grid characteristics stored in the database are obtained by measurement of path loss. Measurements obtained in the offline sampling stage are stored in the fingerprint database to support a matching algorithm performed in the online locating stage.
In the online locating stage, a real-time electrical signal characteristic of the current position of the UE is measured, then a piece of data matches the measured electrical signal characteristic best is found in the fingerprint database by using the matching algorithm, and the position of the UE is determined, where the position of the grid having a characteristic most similar to the measured electrical signal characteristic is determined as the position of the UE.
Precision of locating is significantly affected by the granularity at which the coverage area of the radio network is divided into grids. If the coverage area is divided into grids at a coarser granularity, then the precision of locating is lower. If the coverage area is divided into grids at a finer granularity, then locating requires more computations, longer response time, and a higher processing capacity of a backend database. In practical use, e.g., in indoor scenarios such as shopping malls and airports, different local areas require different precision of locating, so different local areas of an entire coverage area are often required to be divided into grids at different granularities. On the other hand, even if different local areas of an entire coverage area are divided into grids at a same granularity, precision of fingerprint recognitions of grids at different local areas might be different due to reasons such as radio transmission.
In the conventional RFPM indoor locating method, the difference in recognition precision or in confidence among grids in positional fingerprint matching is not taken into account during online locating (i.e., fingerprint matching) computation, thus degrading the precision of locating.